Abstract 6716: DLL3 TriTCE Co-Stim: A next generation trispecific T cell engager with integrated CD28 costimulation for the treatment of DLL3-expressing cancers
Bibliographic record
Abstract
Abstract Small cell lung cancer (SCLC) is an aggressive neuroendocrine cancer with a poor prognosis and high unmet medical need. Current standards of care, including chemotherapy, targeted therapy and immunotherapy, are associated with limited duration of response. Furthermore, SCLC is characterized by an immunosuppressive microenvironment and poor T cell infiltration which present additional treatment challenges. Delta-like ligand 3 (DLL3) is an inhibitory Notch ligand that is aberrantly overexpressed in SCLC and other neuroendocrine tumors, but minimally expressed in normal tissues, and is a new therapeutic target. Bispecific T cell engagers (TCE) targeting DLL3 have entered the clinic and demonstrated promising anti-tumor activity in SCLC patients; however, we believe there is room to improve on the rate and depth of response. To address treatment challenges and enhance the durability and sustainability of T cell activation, we have engineered a trispecific co-stimulatory T cell engager (TriTCE Co-stim) molecule that optimally engages CD3 and CD28 and redirects and enhances cytotoxic T cell responses to DLL3-expressing tumor cells while maintaining a desired safety profile. A panel of DLL3xCD3xCD28 trispecific antibodies (Abs) was engineered with a conventional anti-CD28 agonist paratope in a variety of Ab formats, geometries, paratope affinities and specificities. Geometry and design features of the tri-specific Abs were informed by prior TriTCE Co-stim molecule optimization1. AzymetricTM and EFECTTM platforms were used to facilitate heterodimeric multi-chain antibody assembly with silenced Fc gamma function. Lead TriTCE Co-stim Abs were selected for enhanced DLL3-dependent antitumor cytotoxic activity and increased T cell function including T cell activation, proliferation and cytokine production, compared to competitor bispecific TCEs. DLL3-independent activity and cytokine release were assessed with human PBMCs in solid and solution phase assays to evaluate the potential for cytokine release induction. In summary, we have identified multiple TriTCE Co-Stim Ab formats with improved DLL3-dependent cytotoxicity and T cell activation over competitor bispecific TCEs across multiple DLL3-expressing tumors. By screening various Ab formats, geometries and paratope affinities, we have selected lead DLL3 TriTCE Co-stim Abs displaying target-dependent activation of T cells with potent anti-tumor activity which may translate to improved and more durable antitumor responses in the treatment DLL3-expressing cancers. Citation Format: Peter Repenning, Desmond Lau, Diana C. Hernaez, Alec Robinson, Diego P. Escanda, Mariana Rocha, Begonia S. Moreno, John Zhang, Polly Shao, Nichole Escalante, Lisa Newhook, Purva Bhojane, Chayne L. Piscitelli, Paul A. Moore, Thomas Spreter Von Kreudenstein, Nina E. Weisser. DLL3 TriTCE Co-Stim: A next generation trispecific T cell engager with integrated CD28 costimulation for the treatment of DLL3-expressing cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6716.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".